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Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

A range of optical fibre-based sensors for the measurement of ethanol, primarily in aqueous solution, have been developed and are reviewed here. The sensing approaches can be classified into four groups according to the measurement techniques used, namely absorption (or absorbance), external interferometric, internal fibre grating and plasmonic sensing. The sensors within these groupings can be compared in terms of their characteristic performance indicators, which include sensitivity, resolution and measurement range. Here, particular attention is paid to the potential application areas of these sensors as ethanol production is globally viewed as an important industrial activity. Potential industrial applications are highlighted in the context of the emergence of the internet of things (IoT), which is driving widespread utilization of these sensors in the commercially significant industrial and medical sectors. The review concludes with a summary of the current status and future prospects of optical fibre ethanol sensors for industrial use.

A range of optical fibre-based sensors for the measurement of ethanol, primarily in aqueous solution, have been developed and are reviewed here. The sensing approaches can be classified into four groups according to the measurement techniques used, namely absorption (or absorbance), external interferometric, internal fibre grating and plasmonic sensing. The sensors within these groupings can be compared in terms of their characteristic performance indicators, which include sensitivity, resolution and measurement range. Here, particular attention is paid to the potential application areas of these sensors as ethanol production is globally viewed as an important industrial activity. Potential industrial applications are highlighted in the context of the emergence of the internet of things (IoT), which is driving widespread utilization of these sensors in the commercially significant industrial and medical sectors. The review concludes with a summary of the current status and future prospects of optical fibre ethanol sensors for industrial use.

AbstractPreparation of uniform, spherical Li4Ti5O12 with high tap density is significant to achieve a high volumetric energy density in lithium‐ion batteries. Herein, Li4Ti5O12 microspheres with variable tap density and tunable size distribution were synthesized by a newly designed industrial spray‐drying approach. The slurry concentration, sintering time, sintering conditions after spraying, and the effect of lithium/titanium molar ratio on the lithium‐ion (Li+) storage capability were investigated. A narrow particle size distribution of around 10 μm and a high tap density close to 1.4 g cm−3 of the Li4Ti5O12 spheres can be obtained under the optimized conditions. The Li4Ti5O12 spheres can deliver a much higher capacity of 168 mAh g−1 at a rate of 1 C and show a high capacity retention of 97.7 % over 400 cycles. The synthetic conditions are confirmed to be critical for improving the electron conductivity and Li+ diffusivity by adjusting the crystal and spatial structures. As‐prepared high‐performance Li4Ti5O12 is an ideal electrode for lithium‐ion batteries or capacitors; meanwhile, the presented approach is also applicable for preparing other kind of spherical materials.

AbstractPreparation of uniform, spherical Li4Ti5O12 with high tap density is significant to achieve a high volumetric energy density in lithium‐ion batteries. Herein, Li4Ti5O12 microspheres with variable tap density and tunable size distribution were synthesized by a newly designed industrial spray‐drying approach. The slurry concentration, sintering time, sintering conditions after spraying, and the effect of lithium/titanium molar ratio on the lithium‐ion (Li+) storage capability were investigated. A narrow particle size distribution of around 10 μm and a high tap density close to 1.4 g cm−3 of the Li4Ti5O12 spheres can be obtained under the optimized conditions. The Li4Ti5O12 spheres can deliver a much higher capacity of 168 mAh g−1 at a rate of 1 C and show a high capacity retention of 97.7 % over 400 cycles. The synthetic conditions are confirmed to be critical for improving the electron conductivity and Li+ diffusivity by adjusting the crystal and spatial structures. As‐prepared high‐performance Li4Ti5O12 is an ideal electrode for lithium‐ion batteries or capacitors; meanwhile, the presented approach is also applicable for preparing other kind of spherical materials.

Abstract As a burgeoning theoretical framework, energy justice has been mostly focused on the energy transition in Western countries, where socio-political settings are largely featured by liberalism and democracy, leaving an obvious gap in its application in other socio-political contexts. As a major energy consumer and a leader of the global low-carbon transition, China is characterized by a distinctive socio-political regime. An array of grand strategies to transform its coal-dominant energy structure have been initiated to ameliorate deteriorating environmental crises in particular and materialize a low-carbon transition in general. Based on extensive evidence, this article incorporates the energy justice framework into the analysis of an ongoing energy transition project in rural Northern China. It contributes to the related research in three dimensions. First, empirically, it demonstrates that the coal-to-gas heating transition project has been swamped with social injustices; the absence of measures to address these would lead this mega-project to profound failure. Second, theoretically, it illustrates that the concerns of justice are even more paramount in an authoritarian context where policy processes are characterized by strong political-administrative intervention and the pursuit of efficiency at all cost. In light of this, it stresses the indispensable role of restorative justice as a core tenet in achieving energy justice in authoritarian socio-political contexts, such as China. Third, this study advocates expanding the evaluation parameters of authoritarian environmentalism to include social consequences.

Abstract As a burgeoning theoretical framework, energy justice has been mostly focused on the energy transition in Western countries, where socio-political settings are largely featured by liberalism and democracy, leaving an obvious gap in its application in other socio-political contexts. As a major energy consumer and a leader of the global low-carbon transition, China is characterized by a distinctive socio-political regime. An array of grand strategies to transform its coal-dominant energy structure have been initiated to ameliorate deteriorating environmental crises in particular and materialize a low-carbon transition in general. Based on extensive evidence, this article incorporates the energy justice framework into the analysis of an ongoing energy transition project in rural Northern China. It contributes to the related research in three dimensions. First, empirically, it demonstrates that the coal-to-gas heating transition project has been swamped with social injustices; the absence of measures to address these would lead this mega-project to profound failure. Second, theoretically, it illustrates that the concerns of justice are even more paramount in an authoritarian context where policy processes are characterized by strong political-administrative intervention and the pursuit of efficiency at all cost. In light of this, it stresses the indispensable role of restorative justice as a core tenet in achieving energy justice in authoritarian socio-political contexts, such as China. Third, this study advocates expanding the evaluation parameters of authoritarian environmentalism to include social consequences.